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- W3009917940 abstract "Cardiovascular diseases are one of the common diseases that threaten human health. The classification and identification of abnormal rhythms in ECG is essential to improve the successful cure rate of patients. Therefore, an automatic ECG recognition method based on BP neural network is proposed. Firstly, the electrocardiogram is de-dried by using the bior wavelet to eliminate the baseline drift and high-frequency noise of the ECG. Then, the QRS and TP waves are detected by the double-spline wavelet, and their 6 amplitude features and 15 distance features are extracted. Finally, the optimal BP neural network model is selected to identify the ECG signals. The experimental results show that the correct rate of classification and recognition of ECG signals is more than 99%, and the correct recognition of Type 5 is greatly improved, and it is better than SVM and convolutional neural network." @default.
- W3009917940 created "2020-03-13" @default.
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- W3009917940 date "2019-11-01" @default.
- W3009917940 modified "2023-09-27" @default.
- W3009917940 title "ECG Automatic Identification Method based on BP Neural" @default.
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- W3009917940 doi "https://doi.org/10.1109/iccce48422.2019.9010793" @default.
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